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[WACV 2024] Complex Organ Mask Guided Radiology Report Generation

This is the implementation of Complex Organ Mask Guided Radiology Report Generation. [Paper] [Poster] [Video]

Abstract

contents

Requirements

conda env create -f environment.yaml # method 1
pip install -r requirements.txt # method 2

pycocoevalcap

Download evaluation metrics
https://github.com/zhjohnchan/R2GenCMN/tree/main/pycocoevalcap

|-- COMG_model
|   |-- data
|   |-- pycocoevalcap
|   |-- logfile_saved
|   |-- models
|   |-- modules
|   |-- preprocess_mask
|   |-- results
|   |-- compute_ce.py
|   |-- ...........

|-- COMG_model_RL
|   |-- data
|   |-- pycocoevalcap
|   |-- logfile_saved
|   |-- models
|   |-- modules
|   |-- preprocess_mask
|   |-- results
|   |-- compute_ce.py
|   |-- ...........

Tips:

  1. Please make sure all files exist (if not, please create them by yourself) to avoid some mistakes happening.

Datasets

We use two datasets (IU X-Ray and MIMIC-CXR) in our paper

For IU X-Ray, you can download the dataset from here and then put the files in data.

For MIMIC-CXR, you can download the dataset from here and then put the files in data.

Put data into the data file

|-- data
|   |-- IU_xray
|       |-- images
|           |-- .......
|       |-- annotation.json
|       |-- annotation_disease.json
|   |-- mimic_cxr
|       |-- images
|           |-- .......
|       |-- annotation.json
|       |-- annotation_disease.json

Generate Mask

Generate Mask by using Chest X-Ray Anatomy Segmentation model

cd COMG_model/preprocess_mask
bash generate_mask.sh

|-- data
|   |-- IU_xray
|       |-- images
|       |-- annotation.json
|       |-- annotation_disease.json
|   |-- IU_xray_segmentation (generated)
|       |-- CXR1_1_IM-00001
|       |-- mask files .......
|   |-- mimic_cxr
|       |-- images
|       |-- annotation.json
|       |-- annotation_disease.json
|   |-- mimic_cxr_segmentation (generated)
|       |-- images
|           |-- mask files ......

Tips:

  1. iu-xray mask: almost 147 G and 30 min
  2. mimic-cxr mask: almost 179 G and 5 h

Train & test - The First stage

# IU xray
cd COMG_model
bash train_iu_xray.sh
bash test_iu_xray.sh
# mimic-cxr
bash train_mimic_cxr.sh
bash test_mimic_cxr.sh
# visualization - mimic-cxr
bash plot_mimic_cxr.sh

Train & test - RL - The Second stage

# IU-xray
# train
cd COMG_model_RL 
bash scripts/iu_xray/run_rl.sh
# test
cd ../COMG_model
bash test_iu_xray.sh
# MIMIC-CXR
# train
cd COMG_model_RL 
bash scripts/mimic_cxr/run_rl.sh
# test
cd ../COMG_model
bash test_mimic_cxr.sh

Tips:

  1. Please copy the pycocoevalcap and preprocess_mask files into the COMG_model_RL file.
  2. Please copy the result from COMG_RL to the COMG file for testing results.
  3. This file is the second stage training which means it needs to training based on the first stage model weight.

Compute CE Metrics

Please follow R2GenCMN's work and his code.

JSON files and Checkpoints Download

You can download the JSON files needed and the models we trained for each dataset from here

Result

contents

Citation

If you find this project useful in your research, please cite the following papers:

@InProceedings{Gu_2024_WACV,
    author    = {Gu, Tiancheng and Liu, Dongnan and Li, Zhiyuan and Cai, Weidong},
    title     = {Complex Organ Mask Guided Radiology Report Generation},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {7995-8004}
}

@InProceedings{Gu_2025_WACV,
    author    = {Gu, Tiancheng and Yang, Kaicheng and An, Xiang and Feng, Ziyong and Liu, Dongnan and Cai, Weidong},
    title     = {ORID: Organ-Regional Information Driven Framework for Radiology Report Generation},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    year      = {2025},
}

Acknowledgement

Our project references the codes in the following repos. Thanks for their works and sharing.